@article{(Open Science Index):https://publications.waset.org/pdf/10012368,
	  title     = {Two Class Motor Imagery Classification via Wave Atom Sub-Bants},
	  author    = {Nebi Gedik},
	  country	= {},
	  institution	= {},
	  abstract     = {The goal of motor image brain computer interface research is to create a link between the central nervous system and a computer or device. The most important signal for brain-computer interface is the electroencephalogram. The aim of this research is to explore a set of effective features from EEG signals, separated into frequency bands, using wave atom sub-bands to discriminate right and left-hand motor imagery signals. Over the transform coefficients, feature vectors are constructed for each frequency range and each transform sub-band, and their classification performances are tested. The method is validated using EEG signals from the BCI competition III dataset IIIa and classifiers such as support vector machine and k-nearest neighbors.},
	    journal   = {International Journal of Health and Medical Engineering},
	  volume    = {16},
	  number    = {1},
	  year      = {2022},
	  pages     = {1 - 4},
	  ee        = {https://publications.waset.org/pdf/10012368},
	  url   	= {https://publications.waset.org/vol/181},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 181, 2022},
	}